An analytical cross-sectional study is a type of quantitative, non-experimental research design. These studies seek to "gather data from a group of subjects at only one point in time" (Schmidt & Brown, 2019, p. 206). The purpose is to measure the association between an exposure and a disease, condition or outcome within a defined population. Cross-sectional studies often utilize surveys or questionnaires to gather data from participants (Schmidt & Brown, 2019, pp. 206-207).
Schmidt N. A. & Brown J. M. (2019). Evidence-based practice for nurses: Appraisal and application of research (4th ed.). Jones & Bartlett Learning.
Each JBI Checklist provides tips and guidance on what to look for to answer each question. These tips begin on page 4.
Below are some additional Frequently Asked Questions about the Analytical Cross-Sectional Studies Checklist that have been asked students in previous semesters.
| Frequently Asked Question | Response |
| In regards to Question 5, what exactly is a confounding factor? | A confounder or confounding factor/confounding variable is often referred to as a third variable that could potentially impact the study's results. Read a definition and description here. Confounding factors/variables or confounders may be listed in the study's limitations section or within the study's main results section. |
| For Question 6, how can I tell whether strategies were used to deal with the confounding factors in the study? | Check for multivariate analysis or regression analysis in the study's data analysis/statistical analysis section. Read a definition and description here. |
For more help: Each JBI Checklist provides detailed guidance on what to look for to answer each question on the checklist. These explanatory notes begin on page four of each Checklist. Please review these carefully as you conduct critical appraisal using JBI tools.
Kesmodel U. S. (2018). Cross-sectional studies - what are they good for? Acta Obstetricia et Gynecologica Scandinavica, 97(4), 388–393. https://doi.org/10.1111/aogs.13331
Pandis N. (2014). Cross-sectional studies. American Journal of Orthodontics and Dentofacial Orthopedics, 146(1), 127–129. https://doi.org/10.1016/j.ajodo.2014.05.005
Savitz, D. A., & Wellenius, G. A. (2023). Can cross-sectional studies contribute to causal inference? It depends. American Journal of Epidemiology, 192(4), 514–516. https://doi.org/10.1093/aje/kwac037
Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations. Chest, 158(1S), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012
